code: def test_torch_e2e_state_dict(ray_start_4_cpus, prepare_model):
      def train_func():
          model = torch.nn.Linear(3, 1)
          if prepare_model:
              model = train.torch.prepare_model(model)
          train.report(
              {},
              checkpoint=TorchCheckpoint.from_state_dict(
                  model.state_dict()
              ),
          )
  
      scaling_config = ScalingConfig(num_workers=2)
      trainer = TorchTrainer(
          train_loop_per_worker=train_func,
          scaling_config=scaling_config,
      )
      result = trainer.fit()
  
      # If loading from a state dict, a model definition must be passed in.
      with pytest.raises(ValueError):
          torch_checkpoint = TorchCheckpoint(
              path=result.checkpoint.path,
              filesystem=result.checkpoint.filesystem,
          )
          torch_checkpoint.get_model()
  
imports: import contextlib
  import os
  import tempfile
  import time
  import uuid
  from unittest.mock import patch
  import pytest
  import torch
  import ray
  import ray.train
  from ray.cluster_utils import Cluster
  from ray.train import Checkpoint, RunConfig, ScalingConfig
  from ray.train.examples.pytorch.torch_linear_example import train_func
  from ray.train.torch import TorchCheckpoint, TorchConfig, TorchPredictor, TorchTrainer
  from ray.train.trainer import TrainingFailedError
label_desc: Use image node_id 5 for calling a global function with example usage: test_torch_e2e_state_dict(ray_start_4_cpus, prepare_model) without return types